Name: Python for Simple, Multiple and Polynomial Regression Models
Rating: 4.8 (4 reviews)

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Created byZeeshan Ahmad

Last updated 10/2023

English

What you'll learn

Python Programming for Regression Analysis
Mathematics and Intuition behind Regression Models
Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
Ridge Regression
Least Square Regression
Regression by Gradient Descent

Course content

5 sections • 41 lectures • 6h 43m total length

Introduction of the Course2:30
Course Outline2:42
Course Material0:01

Introduction of the Section0:50
Installing Python Package4:25
Install python using the Anaconda individual edition by downloading the exe from anaconda.com and running the installer. Launch Anaconda Navigator and Jupyter notebook to start a new project.
Introduction of Jupyter Notebook14:27
Arithmetic With Python Part-017:54
Arithmetic With Python Part-029:27
Arithmetic With Python Part-037:49
Dealing With Arrays Part-0111:09
Dealing With Arrays Part-0211:26
Dealing With Arrays Part-0321:09
Plotting and Visualization Part-0117:51
Plot and visualize data with matplotlib.pyplot, generate x and y using numpy linspace, and customize color, size, labels, legend, and title for clear insights.
Plotting and Visualization Part-0215:03
Explore plotting basics with numpy and matplotlib: set x and y axis limits, customize ticks, remove axes, apply styles, and enhance plots with grids, markers, and legends.
Plotting and Visualization Part-0313:34
Learn to create and arrange multiple plots with subplots in Python using NumPy and Matplotlib, control spacing with layout, and compare continuous and discrete sine waves in a 2x2 grid.
Plotting and Visualization Part-047:35
Lists In Python20:27
for loops Part-0121:03
for loops Part-0220:36

Slope-Intercept Form7:35
Explore the slope-intercept form y = mx + b, identifying slope m and intercept b from data. See how a linear model fits salary versus experience and makes predictions.
Definition of Regression11:01
Multiple Regression9:41
Least Square Regression Part-016:39
Least Square Regression Part-028:36
Learn how to derive regression parameters using the least squares loss, expand errors, and solve via gradient to obtain the matrix form and bias term.
Least Square Regression Part-032:19
Explore least square regression by deriving equation 16, implement it in Python with and without sklearn, and compare results to understand the mathematics behind regression.
Simple Regression in Python Part-0119:29
Implement least squares regression in Python to estimate the intercept and the weight. Use salary experience data and visualize results with numpy, pandas, and matplotlib.
Simple Regression in Python Part-026:41
Multiple Regression in Python Part-018:07
Multiple Regression in Python Part-0217:06
Multiple Regression in Python Part-039:43
Polynomial Regression7:01
Expand linear regression to polynomial regression by adding x, x^2, and x^3 terms to fit complex data, covering R1 and R2 and addressing underfitting and overfitting.
Polynomial Regression in Python16:51
Summary of Polynomial Regression2:40

Requirements

Basic Knowledge of Mathematics will be helpful

Description

•The focus of the course is to solve Regression problem in python with the understanding of theory and Mathematics as well.

• All the mathematical equations for Regression problem will be derived and during coding in python we will code these equations step by step to see the implementation of mathematics of Regression in python.

• This course is for everyone. A high school student, a university student and
a researcher in machine learning.

• The course starts from the fundamentals of Regression and then we will
move on to next levels with a decent pace so that every student can follow
along easily.

• In this course you will learn about the theory of the Regression,
mathematics of Regression with proper derivations and following all the
steps. Finally, you will learn how to code Regression in python by following
the equations of Regression learned in the theory.

Who this course is for ?

Students learning Data Science, Machine Learning and Applied Statistical Analytics.

Students and Researchers who want to switch from Matlab and Other Programming Languages to Python.

Students and Researchers who know about the theory of Regression Analysis but don't know how to implement in Python.

Every individual who wants to learn Simple, Multiple and Polynomial Regression Analysis from scratch.

Who this course is for:

Students learning Data Science and Machine Learning.
Want to switch from Matlab and Other Programming Languages to Python
Students and Researchers who knows about Regression Analysis but don't know how to implement in Python
Every individual who wants to learn Linear Regression from scratch

What you'll learn

Explore related topics

Course content

Introduction3 lectures • 5min

Python Crash Course16 lectures • 3hr 25min

Regression Analysis by Least Square Method14 lectures • 2hr 13min

Regression By Gradient Descent5 lectures • 30min

Overfitting and Regularization3 lectures • 30min

Requirements

Description

Who this course is for: